1. Field of the Invention
This invention is related to the surveillance of a person or a moving object across multiple cameras.
2. Description of the Related Art
Surveillance has traditionally been a highly labor-intensive task, requiring people to monitor banks of cameras. Methods for automating the surveillance task, including identifying interesting events, are being developed. Another surveillance task that can be automated is the task of tracking one particular object, a VIP (e.g., an important visitor, or a suspicious person), or a moving object who has been identified by a user. The VIP or moving object moves from one camera view to another and may also move out of camera view, either for a short time due to a gap in coverage, or for a longer period, such as moving into an unmonitored room. One of the primary ways in which the VIP tracking task differs from the most common tracking tasks is that there are multiple cameras where handoff between cameras occurs. The task of camera handoff when tracking objects has a number of difficulties associated with it, including occlusion, gaps in coverage, and noise in the extracted features. Most current methods, such as a Kalman filter, perform a locally optimal classification at each time or video frame.
A number of approaches have been developed to address various aspects of tracking across multiple cameras. These include matching features using a Kalman filter, a Bayesian formulation with a Markov model (but not a hidden Markov model) for transition probabilities, and using a Bayesian Network. Other approaches track objects across multiple cameras by developing models for a set of fixed, uncalibrated cameras that identify the corresponding field of view in overlapping cameras. Another approach uses a ground plane homography (corresponding points between two cameras) to register cameras.
Hidden Markov Models (HMM) have been used for tracking simple targets, where the state sequence indicates the trajectories (location, velocity, appearance, scale) of the objects. HMMs have also been used to model how two agents interact by specifically creating features that are a function of the two agents. HMMs have also been used for tracking human poses, but not for using states that are related to camera views.